• Own the hardest modeling and architecture in your team's scope — ontology objects (types, properties, link types, and actions) that model your part of the business as it actually operates, and the dimensional and semantic models (e.g., Looker/LookML) that serve them downstream • Act as a technical thought partner to the product and business leaders your team supports: understand their goals deeply and translate ambiguous or conflicting business needs into clear, durable technical plans • Take end-to-end ownership of your team's most business-critical initiatives, where deep semantic and architectural judgment is the differentiator • Align your team's models with shared representations of core entities (customer, policy, claim) so they stay consistent and interoperable across the mesh — partnering with the Principal Engineer and peers where definitions are cross-cutting • Define the modeling patterns, naming conventions, and reference implementations your team builds on, and contribute them back to the discipline's shared standards • Drive data-as-a-product expectations within your team's scope — ownership, contracts, documentation, and reliability for what your team owns • Partner with domain data engineers to shape the data contracts and pipelines that feed clean, well-defined ontology objects, and surface upstream issues that degrade your team's models • Raise the technical bar through model and design review, pairing, mentorship, and contributions to hiring and onboarding • Set your team's patterns for applying Claude and Claude Code to analytics engineering work, and design the ontology and semantic layer to be AI-consumable so tools like Databricks Genie can reason over your team's data reliably

• Act as a subject matter expert (SME) and deliver training to the cross functional teams to enable business users to make data-driven decisions. • Deliver direct analytical insights like dashboards and ad-hoc analyses to business stakeholders • Collaborate with the teams across the company to understand their use cases and deliver high value data tools • Live and breathe SQL • Ensure data quality and freshness at every step of the pipeline for data trust and consistency • Create reverse ETL flows to make modeled data directly to stakeholders in the tools they use to foster fast and informed decision making • Define and build robust DataOps pipelines and data expectations to ensure the effective delivery of data to all internal data services • Explore, propose, and integrate new data sources and software solutions into the reporting environment • Contribute to data-driven culture at BetterHelp by directly training stakeholders as well as creating resources such as documentation for empowering others to perform their own analyses • Enjoy great teamwork, have lots of fun, and take pride in building a world-class product that makes a difference in people's lives. • Partner with data and machine learning engineers and work with a modern data stack: Airflow, FiveTran, Snowflake, dbt, and Looker

• Own Reporting Reliability & Data Quality Ensure dashboards and reports are accurate, reliable, and always available Implement monitoring, alerting, and SLAs for critical reporting assets Investigate and resolve data issues with a clear root-cause analysis • Build Scalable Data Models Design and optimize SQL transformations and data models Improve the performance of datasets and reporting queries Reduce duplication by centralizing business logic in the data layer • Deliver High-Impact BI Solutions Build and maintain dashboards, reports, and analytical tools Translate business needs into scalable BI solutions Deliver projects with clear estimation and predictable execution • Ensure Data Trust & Observability Implement data validation checks and anomaly detection Proactively identify issues before they impact stakeholders Improve overall data quality across pipelines and reporting • Partner with the Business Act as a trusted partner for Marketing and commercial teams Define and maintain key metrics (CAC, ROAS, conversion funnels, etc.) Generate insights that directly influence business decisions

Role Description As a Fullstack Engineer (Semantic / Analytics) (m/f/d), you will own the business meaning of data and make it reusable across analytics, BI, APIs, semantic access, and AI-powered experiences. You will work across semantic modeling, KPI logic, reusable data models, business-facing data exposure, and collaborate closely with platform, AI, solution, and business teams. The platform will continue to evolve toward broader support for unstructured data and lakehouse-style capabilities. We work spec-driven, use AI-assisted engineering tools such as Claude Code and Cursor, follow “You build it, you run it”, and expect strong specialization combined with fullstack ownership. - Build the semantic foundation for data products - Build and evolve the semantic layer in dbt and Snowflake semantic views, including business entities, metrics, dimensions, and reusable data models - Define KPIs, business logic, canonical data definitions, and semantic consistency standards together with business and product stakeholders - Help shape how semantic data products are exposed consistently across internal and external platform capabilities - Ensure business entities, KPIs, and metrics are clearly and consistently defined across the platform - Make curated data usable across BI, APIs, and AI - Expose curated data for BI tools such as Amazon QuickSight and Apache Superset, APIs, downstream product use cases, and AI consumption including Snowflake Cortex AI - Support AI use cases through feature shaping, context structuring, semantic enrichment, and business-grounded data preparation - Collaborate with the AI Engineer to ensure agentic experiences are grounded in meaningful, well-structured business data - Help ensure BI, APIs, and AI use cases rely on the same trusted semantic foundations in Snowflake - Work with reliable, fresh, and governed data - Work with near-real-time data ingested from PostgreSQL into Snowflake via Snowflake Openflow - Ensure semantic models reflect fresh, reliable data from operational systems - Align with solution teams on data contracts, source semantics, and integration expectations - Help define validation rules, data trust practices, lineage support, and consistency controls - Collaborate across platform, product, and engineering - Work closely with the Data & Platform Architect and Data & Platform Engineer to build semantic models on reliable, scalable Snowflake foundations - Collaborate with platform, AI, solution, product, and business-facing teams - Help the company build a reusable semantic layer that scales with future platform growth - Apply spec-driven development, AI-assisted engineering workflows, and end-to-end production ownership Qualifications - Very strong hands-on SQL skills and broad, deep database knowledge, including data modeling - Strong hands-on experience with Snowflake, including Snowflake semantic views - Hands-on experience with dbt at scale for transformations and analytics engineering best practices - Experience with PostgreSQL as a source for structured business data - Experience building semantic layers, reusable metrics, canonical data models, analytics engineering assets, KPIs, business logic, and data definitions with stakeholders - Experience exposing data for BI, APIs, downstream product use cases, and AI or analytics consumption - Experience defining or supporting data contracts, validation rules, semantic consistency standards, data quality, lineage, and trust practices Requirements - Experience with near-real-time or CDC ingestion, ideally Snowflake Openflow or comparable tools such as Fivetran, Debezium, or Kafka - Strong Python skills - Experience building APIs and services such as REST or GraphQL - Experience exposing data and tools through interfaces such as MCP servers - Solid AWS stack know-how - Experience with BI tools such as Amazon QuickSight, Apache Superset, Looker, Tableau, Power BI, or similar platforms - Strong understanding of how data should be structured for AI, analytics, semantic access, and product consumption - Comfortable with structured, spec-driven delivery and AI-assisted development workflows Benefits - 30 days of paid vacation - EGYM Wellpass membership to support your work-life balance - Flexible working models to better balance work and personal life - Inspiring office spaces in the heart of Munich - Flexible remote work from home or anywhere within Germany - A professional, welcoming, and highly motivated team - Collaboration at eye level with an open feedback culture - An environment where people support each other and grow together - Short decision-making paths and real opportunities to shape things - Freedom to contribute and implement your own ideas - A high level of ownership and responsibility